code: def test_maybe_promote_float_with_int(
      float_numpy_dtype, any_int_numpy_dtype
  ):
      dtype = np.dtype(float_numpy_dtype)
      fill_dtype = np.dtype(any_int_numpy_dtype)
  
      # create array of given dtype; casts "1" to correct dtype
      fill_value = np.array([1], dtype=fill_dtype)[0]
  
      # filling float with int always keeps float dtype
      # because: np.finfo('float32').max > np.iinfo('uint64').max
      expected_dtype = dtype
      # output is not a generic float, but corresponds to expected_dtype
      exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[
          0
      ]
  
      _check_promote(
          dtype, fill_value, expected_dtype, exp_val_for_scalar
      )
  
imports: import datetime
  from decimal import Decimal
  import numpy
  import pytest
  from pandas._libs.tslibs import NaT
  from pandas.core.dtypes.cast import maybe_promote
  from pandas.core.dtypes.common import is_scalar
  from pandas.core.dtypes.dtypes import DatetimeTZDtype
  from pandas.core.dtypes.missing import isna
  import pandas
label_desc: Use image node_id 5 for calling a global function with example usage: test_maybe_promote_float_with_int(float_numpy_dtype, any_int_numpy_dtype) without return types